Abstract:

Sparse matrix vector multiplication (SpMV) is an important issue in solving sparse linear systems. The sparse features and the low computing density lead to low computation efficiency. Regarding the irregularities of the sparse matrixes, some hybrid storage formats are used to compute SpMV to improve the compression efficiency and expand the range of adaptation.  HYB is a hybrid compression format of ELL and COO formats, and is widely used on SpMV because of its stable performance. With the common application of parallel computing on GPUs and multicore CPUs, the heterogeneous computing system based on CPU+GPU is accepted. The ELL of HYB is assigned to the GPU for processing and the COO of HYB is assigned to the CPU, which can take full advantages of both CPU and GPU computing resources to improve the utilization efficiency of computing resources. In this paper, based on the analysis of the characteristics of the CPU + GPU heterogeneous computing model, we propose some optimization strategies to improve the performance of SpMV in the heterogeneous computing environment.

Key words: GPU;sparse matrix;SpMV;CUDA;heterogeneous computing